Skip to main content

Python package for remnant black hole properties using perturbation theory and NR

Project description

BHPTNR_Remnant

BHPTNR_Remnant is an easy-to-use python package to efficiently predict the remnant mass, remnant spin, peak luminosity and the final kick imparted on the remnant black hole directly from the gravitational radiation using GPR fits. These fits have been built on the remnant data calculated from numerical relativity informed black hole perturbation theory based waveforms.

Tutorials

Example usage is provided in tutorial.ipynb notebook.

Using pip

This package is available in Pypi : https://pypi.org/project/BHPTNRremnant/ To install : pip install BHPTNRremnant

A web-based example jupyter notebook tutorial is hosted here : https://github.com/tousifislam/BHPTNR_Remnant/blob/main/tutorials/tutorial.ipynb Once installed, import is as import BHPTNRremnant

NR informed ppBHPT based surrogate fits can then be used as

# import surrogate fits
from BHPTNRremnant.remnant import BHPTNRSurRemnant

# instantiate the class
fits = BHPTNRSurRemnant()
# evaluate the fits at mass ratio q
# final mass, final spin, kick velocity and luminosity distance and their associated GPR fit error
mf, mferr, sf, sferr, vf, vferr, Lp, Lperr = fits.evaluate_fit(15)
print(mf,sf, vf,Lp)

One can also use the analytical fits for the kick velocities as following:

from BHPTNRremnant.analytical_fits import BHPTAnalyticalFits

# instantiate analytical fits
fit_obj = BHPTAnalyticalFits(q=15, a=0)
# evaluate kick velocity form Sundararajan, Khanna & Hughes
vf = fit_obj.SKH_kick_fit()
print(vf)
# evaluate kick velocity form Sundararajan, Khanna & Hughes
# corrected for the small mass ratio
vf = fit_obj.SKH_kick_fit_small_q_corrected()
print(vf)
# evaluate kick velocity form Islam, Field & Khanna
vf = fit_obj.IFK_kick_fit()
print(vf)

It is also possible to obtain analytical results at extreme mass ratio limit:

from BHPTNRremnant.point_particle import PointParticle

# instantiate point particle limit results
pp = PointParticle(q=1000, a=0)
# obtain final mass and final spin
pp.obtain_final_state()

Citation guideline

If you make use of any module from the Toolkit in your research please acknowledge using:

This work makes use of the Black Hole Perturbation Toolkit.

If you make use of the BHPTNRSur models please cite the following paper:

@article{Islam:2022laz,
    author = "Islam, Tousif and Field, Scott E. and Khanna, Gaurav.",
    title = "{Remnant black hole properties from numerical-relativity-informed perturbation theory and implications for waveform modelling}",
    eprint = "https://arxiv.org/abs/2301.07215",
    archivePrefix = "arXiv",
    primaryClass = "gr-qc",
    month = "1",
    year = "2023"
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

bhptnrremnant-0.0.3.tar.gz (418.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

bhptnrremnant-0.0.3-py3-none-any.whl (420.6 kB view details)

Uploaded Python 3

File details

Details for the file bhptnrremnant-0.0.3.tar.gz.

File metadata

  • Download URL: bhptnrremnant-0.0.3.tar.gz
  • Upload date:
  • Size: 418.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.18

File hashes

Hashes for bhptnrremnant-0.0.3.tar.gz
Algorithm Hash digest
SHA256 5f1807ff586895a2e267e4ad3440d7a2560af3610150dc5eeb20dea6b541abcf
MD5 1f83871bf4253afa8633fa54954d42a2
BLAKE2b-256 ea3783f0fb24901252896f04473b2d765a92f5df3d5717eb8440c5a6f883551a

See more details on using hashes here.

File details

Details for the file bhptnrremnant-0.0.3-py3-none-any.whl.

File metadata

  • Download URL: bhptnrremnant-0.0.3-py3-none-any.whl
  • Upload date:
  • Size: 420.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.18

File hashes

Hashes for bhptnrremnant-0.0.3-py3-none-any.whl
Algorithm Hash digest
SHA256 55985f7cbca541f889dfd1358359ec7d18c1408c46bd26886a84ff078aebbd3f
MD5 79533d8ed0ecc33db5c50dfbd973593a
BLAKE2b-256 fb735306fc96058cb47313f76d59f9d5b92708e6958edf84d4c93a2f1859e6f3

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page